Mud Pumping Research Articles

Analisis Tingkat Risiko Penyebab Rel Patah pada Jalur Kereta Api Wilayah Divre IV Tanjung Karang

The increase in the volume of passengers and goods in rail transportation in Indonesia has an impact on the condition of railway infrastructure, especially railway infrastructure. The main focus of this condition is the risk of rail damage, especially broken rails. So a holistic assessment approach is needed to identify the root causes and formulate appropriate treatment and improvement strategies. The aim of the research is to identify the factors causing the rail to break and analyze the causes. This research uses a risk level value analysis method by multiplying the results of probability and impact, thus producing a risk value. Risk variables such as extreme rain events, expansion in the alignment area, and mud pumping are high level risks. This is followed by medium, low and very low risks, according to the classification of each risk variable analyzed. Therefore, the priority scale for handling broken rail repairs is grouped based on the risk level value starting from the highest risk to the lowest risk. The conclusion is that risk variables such as extreme rain produce a risk value of IDR 296,881,200 with a probability value of 0.99% and an impact of IDR 29,988,000,000, expansion in the straight area produces a risk value of IDR 376,992,000 with a probability value of 1.10% and impact IDR 34,272,000,000, and mud pumping produces a risk value of IDR 394,128,000 with a probability value of 1.15% and impact IDR 34,272,000,000, these three risk variables are classified as high risk.

Semi-Conv-DETR: A railway ballast bed defect detection model integrating convolutional augmentation and semi-supervised DETR

Railway ballast bed defects, including subsidence, mud pumping, and abnormal water, pose significant safety risks by destabilizing the railway ballast beds. Timely detection and repair of railway ballast bed defects are vital for safeguarding the security of both the trains and their passengers. Ground-Penetrating Radar (GPR) is widely used for railway ballast beds inspection and evaluation owing to its high speed and non-destructive characteristics. However, GPR image data contain considerable noise, and the distinct shapes and sizes of each ballast bed defect renders it challenging to apply a unified data annotation standard, which hampers the development of railway ballast bed defect detection models. Considering the distinct wave-like characteristics of GPR data and the vaguely contours of the defects to be identified, we propose a convolutional augmentation operation tailored for GPR images. Furthermore, we also investigate Semi-Supervised Learning by employing limited annotated railway ballast bed inspection data along with a vast amount of unlabeled data to joint train the DETR detection model. To sum up, we proposed a semi-supervised DETR model supplemented with convolutional augmentation for railway ballast bed defect detection, termed as Semi-Conv-DETR model. Experimental outcomes indicate that Semi-Conv-DETR shows an improvement of 58.6 % in accuracy when compared to the classical Faster-RCNN model.

Subgrade soil response to rail loading: Instability mechanisms, causative factors, and preventive measures

The demand for more efficient heavy-haul rail networks over soft subgrades poses significant geotechnical challenges and requires a comprehensive understanding of stress conditions as well as the failure potential of subgrade soil under moving wheel loads and increasing rail speeds. Unfavourable stress conditions in the subgrade can result in various types of failures, three of which are identified in this article: (i) excessive plastic settlement, (ii) progressive shear failure, and (iii) subgrade fluidisation (mud pumping). Through a series of advanced testing schemes using cyclic triaxial, hollow cylinder, and an in-house dynamic filtration apparatus, critical stress conditions and soil characteristics prone to subgrade instability are discussed. The results demonstrate that under adverse combinations of loading frequency (f) and cyclic stress ratio (CSR) the continuous application of cyclic loads can lead to an unstable state of soil where excess pore pressure and axial strain increase rapidly. This study also reveals that low to medium plasticity soils (PI < 22) are more vulnerable to subgrade fluidisation, where the rapid internal migration of pore water transforms the upper soil to a fluid-like state with substantial loss in soil stiffness. The layered response of soil through dynamic filtration tests showed larger hydrodynamic forces induced by differential hydraulic gradients in the top layer during cyclic loading causes moisture to move upwards. Various factors that can influence soil instability such as the degree of compaction degree, clay content, soil fabric and stress rotation are also addressed in this paper. Finally, novel solutions for stabilising subgrade such as a vertical drain-composite system and the use of eco-friendly biopolymers are presented.

A prediction model for railroad ballast resilient modulus considering cyclic loading duration ratio

Railroad ballast aggregate significantly contributes to load distribution, drainage, and track resilience. Over time, the resilience degrades due to factors like ballast breakage, permanent deformation, and mud pumping, necessitating regular, resource-intensive maintenance. Thus, investigating the influential factors of resilient modulus and accurately correlating them can assist in cost-effective maintenance planning. This study used large-scale triaxial tests with varied loading waveforms to investigate their impact on resilient modulus. A new index, the Cyclic Loading Duration Ratio (CLDR), was introduced to quantify loading waveforms with different rest periods. Our experimental results indicate that the cyclic loading duration period controls the resilient modulus of granular material by varying the loading rates, while unloading duration and rest periods do not affect the resilient modulus. With increasing CLDR, resilient modulus initially decreases, but begins to ascend when CLDR falls above 0.20. Building on these findings, an empirical correlation model incorporating the CLDR for the resilient modulus of railroad ballast is proposed. The validation results show this proposed model outperforms existing ones in correlating the resilient modulus with cyclic loading waveforms.

Critical speed of ballasted railway tracks: Influence of ballast and subgrade degradation

Track substructure layers such as ballast and subgrade involve progressive degradation with time in relation to the loading history. Degradation of track components directly influences the dynamic track response including the displacements and stresses. Determining the influence of real-world substructure degradation problems such as particle breakage, ballast fouling and mud pumping on the critical speed of a given track is essential to design ballasted tracks for high-speed passenger and freight trains. In this paper, a coupled rheological-continuum model is developed to predict the track dynamic response for different train speeds and axle loads. In this novel approach, field dispersion curves obtained from MASW (Multichannel Analysis of Surface Waves) testing can be adopted along with the proposed analytical model to predict the critical speed of track as well as the corresponding dynamic stress state in the substructure layers. Analytical modelling shows that problems such as ballast fouling and mud pumping lead to reduction in the computed critical speed, while also amplifying the dynamic stress response at lower train speeds. The influence of substructure degradation is found to be more prominent for heavy-haul (freight) trains imparting higher axle loads, when compared to passenger trains. Practical insights highlighting the influence of track improvements such as placing a thicker ballast layer and the addition of a capping layer on the critical speed are presented in this study with the objective of rejuvenating future track design.

A Georeferenced Dataset for Mapping and Assessing Subgrade Defects in China’s High-Speed Railways

China has the world’s longest high-speed rail (HSR) network, marked by dense transportation and complex operations. However, frequent train use coupled with extreme weather conditions has led to rising subgrade issues. Existing railway defect records suffer from inconsistency, hindering direct applicability. Currently, there is a lack of a relevant dataset dedicated to HSR subgrade defects. To bridge this gap, we developed a comprehensive georeferenced dataset that encompasses defect records extracted from peer-reviewed literature published between 1999 and 2023 in China. Rigorous quality control procedures were implemented to eliminate duplicate data and ensure the accuracy of the dataset. The dataset consists of georeferenced records for eight different defects, spanning across 661 locations and categorized at various scales, ranging from provinces to townships. The most commonly reported defect types include subgrade settlement, frost damage, uplift deformation, and mud pumping. This dataset provides a comprehensive map of historical subgrade defects affecting high-speed railways in China. It could facilitate operational risk assessments and the prediction of subgrade performance.

Multi-defect risk assessment in high-speed rail subgrade infrastructure in China

This study addresses the escalating risk of high-speed railway (HSR) infrastructure in China, amplified by climate warming, increased rainfall, frequent extreme weather, and geohazard events. Leveraging a georeferenced dataset of recent HSR defects obtained through an extensive literature review, we employ machine learning techniques for a quantitative multi-defect risk assessment. Climatic, geomorphological, geohydrological, and anthropogenic variables influencing HSR subgrade safety are identified and ranked. Climatic factors significantly impact frost damage and mud pumping, while geomorphological variables exhibit greater influence on settlement and uplift deformation defects. Notably, frost damage is prevalent in the northeast and northwest, mud pumping along the southeast coast, and settlement and uplift deformation in the northwest and central areas. The generated comprehensive risk map underscores high-risk zones, particularly the Menyuan Hui Autonomous and Minle County sections of the Lanzhou-Urumqi HSR, emphasizing the need for focused attention and preventive actions to mitigate potential losses and ensure operational continuity.

Study on characteristics and mechanism of subgrade mud pumping under heavy-haul train loads

Study on characteristics and mechanism of subgrade mud pumping under heavy-haul train loads

NUMERICAL METHODS OF HYDRO-DYNAMIC COUPLING IN SATURATED SOIL AND ITS APPLICATION TO RAILWAY ENGINEERING

Recent field case study shows that the roadbed of ballastless high-speed railway experienced water-induced defect such as excessive fines pumping and even local subgrade-track contact loss affecting the normal operation of highspeed train due to water immersion through gaps of waterproof materials in expansion joints between the concrete base, particularly in rainy seasons. However, the study about the dynamic behavior of high-speed railway subgrade involving water is currently rare. Based on the theory of fluid dynamics in porous medium and the vehicle-track coupling vibration theory, a numerical method of hydraulic-dynamic coupling was established to evaluate the dynamic responses of saturated roadbed surface layer under the high-speed train loading with the validation by comparing the calculated values and field data. The temporal and spatial characteristics of dynamic behaviors (stress, pore water pressure, seepage velocity, displacement) of saturated roadbed surface layer are fully discussed. Also, the effects of train velocity, permeability, on aforementioned dynamic responses of the saturated roadbed surface layer are evaluated. The study shows that improving the drainage of ballastless track roadbed has a significant effect on minimizing the mud pumping of ballastless track, and the influence zone of hydraulic-mechanical coupling is mainly within 0.1 m of the roadbed.

Technical Summary of Tunnel Mud Pumping Treatment and a Method of Pressure Reduction by Water Release

With the rapid development of China’s economy and the increasing demand for resources in various regions, the speed and volume of railway operations are increasing, and diseases such as mud pumping and mud pumping in railway tunnels are becoming more and more serious. Although various construction units have taken diverse measures to rectify the diseases, they often have poor results, repeated rectification, and repeated failure, which consumes a lot of financial and material resources but cannot cure the mud pumping disease, allowing the disease to seriously endanger the driving safety of the line. This paper is based on the long-established loess tunnel base mud pumping disease treatment project in the Lanzhou Railway Bureau of the China Railway. Firstly, the influence of train load, structural fracture, and tunnel drainage systems on the mechanism of disease is expounded. Then, the key technologies and processes of the commonly used disease treatment schemes are sorted out and summarized, and the advantages and disadvantages of the commonly used schemes and the additional drainage tunnel and self-priming pump technology proposed in this paper are compared in detail. The analysis shows that the universally used treatment technology applies to the treatment of local mud pumping disease in the tunnel, and there are shortcomings in these measures for the long-established tunnel. The drainage tunnel and self-priming pump technology can reduce and maintain the small content of groundwater in the surrounding rock of the tunnel basement and can resolve the mud pumping disease of the loess tunnel basement from the root. At present, this technology has been applied to many tunnel projects and has achieved certain comprehensive benefits, which could provide a reference for similar projects.

The effect of biopolymer treatment on the potential instability of a soft soil under cyclic loading

Mud pumping is frequently reported as a serious concern in soft soil foundations subjected to repeated train loads. Although conventional methods such as lime and cement treatment can be used to alleviate this problem, they can cause considerable environmental damage. This paper presents the novel use of an eco-friendly biopolymer, Xanthan Gum (XG), to enhance the performance of soft subgrade under cyclic loading in the context of railways. A series of cyclic triaxial tests were carried out on natural subgrade soil with a history of mud pumping and the results were compared with XG-treated specimens subjected to typical loading conditions representative of heavy-haul trains. The efficacy of XG was examined by varying cyclic stress ratio (CSR) and XG content for different test specimens. The results indicate that XG, with its hydrophilic nature and densification of soil specimens through the pore filling and particle bonding process, can help reduce the accumulated excess pore pressure (EPP) effectively, thereby substantially improving soil resistance to cyclic loading and mitigating the migration of pore water within the test specimen. The permanent strains are found to be less than 3% despite using a small amount of XG content (0.5%) under a high cyclic stress ratio (CSR = 0.8) and the number of loading cycles up to 50,000 cycles, while the resilient modulus of soil can increase significantly as XG content increases.

Full-scale mud pumping test of ballastless trackbed under train loading

This paper presents a full-scale physical modeling investigation on the mud pumping in ballastless trackbed. The physical model was equipped with a rainfall device to simulate the in situ precipitation and various testing sensors were installed to monitor the dynamic response of the track-subgrade system. Results showed that the whipping of the track structure induced by high-speed train passages caused a separation between the track structure and the underlying roadbed, which allowed rainwater to intrude into the roadbed. The intruded rainwater infiltrated vertically and then permeated laterally from the bottom of the roadbed, eventually saturating the entire roadbed. Train moving loads partially promoted this rainwater infiltration. The excess pore water pressure (EPWP) was formed in the saturated roadbed caused by train moving loads, and it increased with train speeds and loading carriages. A noticeable upward EPWP gradient and a slight longitudinal EPWP gradient towards the expansion gap were also created in the saturated roadbed. Accordingly, both the vertical hydraulic gradient (HG) and longitudinal HG increased with train speeds and loading carriages, most of which, especially the vertical HG, exceeded the critical HG for particle migration with a diameter smaller than 7.1 mm. It caused severe upward particle migration and slight longitudinal particle migration towards the expansion gap. This spatial fine particle migration process, known as mud pumping, was driven by the HG-induced seepage force and further promoted by the EPWP amplification induced by high-speed train passages. Mud pumping resulted in a significant decrease in stiffness of the trackbed and a noticeable increase in vibration velocity of the track slab.

An explainable artificial intelligence approach for mud pumping prediction in railway track based on GIS information and in-service train monitoring data

Automatic and timely identification of mud pumping is important for the reliability and safety of railroads. The current mud pumping prediction model is based on monitoring the dynamic response of railway tracks. The essential geotechnical trigger factors such as the hydrological conditions are not well-considered in these prediction models, as that information is hard to be quantified, which unavoidably reduces the accuracy of the prediction. This paper proposes to utilize the Geographic Information System (GIS) to quantify the hydrological information along railway tracks. Through GIS analysis, the hydrological variables including elevation, near-river distance, rainfall, sink depth, and soil types are estimated and combined with in-service train monitoring data for model development. To deal with multi-attribute data, a dual-channel neural networks model is proposed to separately mine the characteristics in different attributes data for prediction. To further understand the prediction model, Shapely addictive explanations (SHAP) method is applied to estimate the importance of hydrological variables and reveal the possible relationships between the variables and the probability of mud pumping. The proposed approach is applied to a real-life case from the railway tracks in Australia to validate its effectiveness. The prediction results show that the proposed approach can predict mud pumping with balanced accuracy of 90.84%. The results confirm that integrating of GIS information and monitoring data can generate more accurate prediction and reduce the false prediction rate. Based on the explainable results, it is observed that rainfall is the most important hydrological variable that influences mud pumping occurrence. Apart from rainfall, groundwater-related variables show a greater impact on mud pumping occurrence than surface water-related variables. The explainable results also can help infrastructure managers to identify the most vulnerable sections in railway tracks, which facilitates targeted maintenance planning and track substructure design.

Experimental Study of Influence of Freeze–Thaw Cycles on the Dynamic Properties of Weathered Sand-Amended Expansive Soils

Expanded soils are widely distributed in Xinjiang, China, so roadbeds will inevitably pass through the areas of the expansive soil during road construction. While Xinjiang belongs to the seasonal frozen region, subjected to a freeze–thaw cycle, mud pumping is likely to occur in the subgrade under dynamical load. To study the dynamic properties of expansive soil for a range of freezing–thawing cycles and weathered sand contents, a series of dynamic triaxial tests were performed using a medium-scale true triaxial apparatus equipped with the cyclic loading device. And the maximum dynamic elastic modulus (Edmax) and ultimate dynamic stress amplitude (σdmax) were quantitatively analyzed by the modified Hardin–Drnevich three-parameter model, on which the expression of the damage degree under the joint action of both was established. The results indicate that the dynamic performance of expansive soil is greatly affected by freezing–thawing. Under the same weathered sand content, the maximum dynamic elastic modulus and the maximum dynamic stress amplitude of soil are inversely related to the number of freezing–thawing cycles. And, those two parameters tend to be stable after the sixth freeze–thaw cycle. Under the same number of freeze–thaw cycles, the maximum dynamic elastic modulus and the maximum dynamic stress amplitude firstly decrease and then increase with the increase in the weathered sand content. The critical dosage of weathered sand is 10%, and the strength of the improved soil reached the minimum value under this context. The damage degree model that integrates the effects of freeze–thaw cycles and the weathered sand can better predict the damage degree of soil.

Micromechanical Analysis of Internal Instability during Shearing

Internal instability means that finer particles pass through the constrictions of coarser particles at a hydraulic gradient well below that of heave or piping, rendering the soil ineffective for its intended purpose. The soil could make a transition from an internally stable state to an unstable state or vice versa due to shear-induced deformation. The discrete element method (DEM) is adopted in this study to examine and quantify soil behavior by simulating the quasi-static shear deformation of internally stable and unstable soils at the micro- and macroscales. The dense bimodal specimens were sheared under drained conditions following a constant mean stress path in order to investigate the influence of stress heterogeneity. At the macroscale, the peak deviatoric stress was found to be a function of the fines content and the initial void ratios of the specimens. The development of the average number of contacts per particle and the stress transfer to the finer fraction during shearing are discussed. The simulation results innovatively show that a dense specimen could undergo a transition from an internally stable to an unstable soil as it dilates during shear. These numerical results have significant implications on the importance of real-life situations, such as predicting mud pumping in railroad tracks.

Experimental study on mechanical and frost heave behaviors of silty clay improved by polyvinyl alcohol and polypropylene fiber

Silty clay is widely used as subgrade filler in cold regions, which suffer from frost heave in winter and mud pumping in spring. In this study, polyvinyl alcohol (PVA) and polypropylene (PP) fiber were used to improve the mechanical and frost heave behavior of silty clay in cold regions, and the direct shear test and one-dimensional frost heave test were employed in studying improvement effects. Moreover, improvement mechanisms of PVA and PP fiber were analyzed based on test results. The main findings are as follows. (1) Both PP and PVA can heighten the strength of silty clay and suppress frost heave, but the PVA solution has a more decisive influence on improving mechanical properties than PP fiber. (2) The improvement mechanism of the PVA solution is cementing. The improvement effect of 2% PVA solution is the best, which can increase the shear strength by approximately 40%–60% at different stress levels and decrease the frost heave ratio from 0.89% to 0.16% at optimal water content. (3) For 2% PVA improved samples, 0.25% PP fiber can further increase soil cohesion by approximately 20–30 kPa at different stress levels and further decrease the frost heave ratio from 0.16% to 0.07% at optimal water content. The improvement effect is neglectable when the PP fiber content exceeds 0.25%. Overall, 2% PVA with 0.25% PP fiber is the optimum combination to improve silty clay in cold regions.

Mud loss behavior in fractured formation with high temperature and pressure

Drilling into fractured formations can easily lead to mud loss, which significantly affects drilling efficiency and increases drilling costs. In high-temperature and high-pressure environments, changes in mud properties render mud loss more uncontrollable. On the basis of a mud performance test in a high-temperature and high-pressure environment, and considering the time-varying characteristics of wellbore temperature and pressure and the distribution law of fracture temperature and pressure, a coupling model of temperature and pressure in the wellbore and fracture was established in this study. The developed model was solved numerically using the finite difference approximation, and the characteristics of the mud loss were analyzed. The effects of geothermal gradient, fracture parameters (fracture width, fracture length, and deformation capacity), mud properties (viscosity, yield value, and specific heat capacity), and operating conditions on mud loss were investigated. The model was validated using field measurement-while-drilling tool data of the LD1 well in the South China Sea. The results show that the average errors between the calculated temperature and pressure results of the numerical model and the actual data are 2.45% and 0.57%, respectively, which proves that the model has high accuracy in the prediction of the bottom hole temperature and pressure. A comparison of the predicted data of mud loss with the actual measured data indicates that the average error of the mud leakage model is 4.23%. This also demonstrates that the model has a high accuracy in predicting mud loss. In addition, the results show that the mud performance changes significantly under high-temperature and high-pressure conditions, which has a significant influence on mud loss. The geothermal gradient affected the mud properties and thus significantly affected the mud loss characteristics. The larger the mud pumping rate, the larger the bottomhole pressure difference and the more serious the mud loss. Blindly increasing mud viscosity is not advisable because it may lead to more serious losses. With an increase in the mud yield value, the mud flow resistance in the fractures can be significantly increased to inhibit mud loss. The larger the mud-specific heat capacity, the less mud loss is affected by the temperature. With an increase in the fracture width, the degree of mud loss gradually decreased. The larger the fracture length and deformation capacity, the more serious the mud loss. The fracture length and width could be inverted using the developed model.

Investigation of Water and Soil Migration and Mud Pumping of Subgrades under Traffic Load

Atmospheric precipitation leads to the increase of moisture in the subgrade. The moisture and soil migration in subgrade is a key scientific problem in evaluating the service performance of the subgrade and resolving or preventing mud pumping. Using a self-developed testing system as well as a numerical simulation model, a detailed study of the dynamic hydraulic characteristics, water migration mechanism, fine soil migration mechanism, and mud pumping properties of the subgrade was conducted. The results showed that water migrated into the upper layer of the subgrade under traffic load. Meanwhile, a pressure gradient for the pore water was generated in the subgrade. This kind of pressure gradient is beneficial for the moisture and fine soil migration. With rising groundwater level and increasing traffic load, the porosity of the subgrade soil differs at different depths. The fine soil migration is caused by water migration, which causes new migration channel for water in the subgrade. Then, a circulating system of moisture and fine soil is formed in the subgrade under a traffic load. After that, the upper layer subgrade soil is nearly saturated under the action of traffic load. Then, the nearly saturated soil liquefies instantaneously, becoming mud, under the action of traffic load. However, as the loading time progresses, the moisture and fine soil in the subgrade continue to migrate upward, resulting in the mud being pumped into the ballast and the gradual disappearance of liquefied soils at different depths.

Automatic analysis of railway ground penetrating radar: Using signal processing and machine learning approaches to assess railroad track substructure

Prior to track rehabilitation works on the French railway network, train-mounted ground penetrating radar (GPR) allows for fast and nondestructive data acquisition. Substructure condition evaluation is of paramount importance as it has an impact on the quality of the planned renewal works. The interpretation of GPR data remains a challenging and time-consuming task which requires expert intervention. This research seeks to enhance and automate the analysis of the GPR data using two different approaches. A signal processing approach based on entropy analysis determines the layer thicknesses, evaluates ballast fouling, and locates areas with water retention. Field comparisons showed concordances with the proposed method. The second approach relies on deep learning to detect mud pumping defects. It showed promising results for the detection of high intensity mud pumping.

Water content variation of railway track sub-ballast layer in seasonal frost area: A case study from Finland

Many previous international studies have found a clear relationship between railway track water content and track geometry problems. The proper drainage of substructures is important in ensuring the safe and efficient operation of tracks. This importance will be emphasized in the future with the increasing frequency of extreme weather phenomena and the raising axle weights of train traffic. In many studies, excessive water content has been found to cause excessive geometry deterioration and mud pumping. These problems are usually related to thin substructure layers. However, in this case study, Finland, track substructures are thick due to frost protection requirements and behave differently to thin substructures. In this study, the long-term water content of three instrumented test sites was monitored to study the seasonal and freeze–thaw effects, as well as the effects of drainage improvement made at one site. In a previously reported part of the same research project, the measured water contents were used in an extensive cyclic loading triaxial test series evaluating the loading resistance of substructure materials taken from the same sites. The drainage improvement resulted in a marked decrease in the average water content at the drainage improvement test site. Simultaneously, high momentary water content values also disappeared. The role of capillary water was observed to be less than expected on Finnish railways.